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Optimising large scale public transport network design problems using mixed-mode parallel multi-objective evolutionary algorithms

Rhyd Lewis · Christine L. Mumford · Andrew Olden

22 citations0/0 files storedIEEE Congress on Evolutionary Computation (CEC)

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Transportation Planning and Optimization Metaheuristic Optimization Algorithms Research Advanced Multi-Objective Optimization Algorithms

Abstract

In this paper we present a novel tool, using both OpenMP and MPI protocols, for optimising the efficiency of Urban Transportation Systems within a defined catchment, town or city. We build on a previously presented model which uses a Genetic Algorithm with novel genetic operators to optimise route sets and provide a transport network for a given problem set. This model is then implemented within a Parallel Multi-Objective Genetic Algorithm and demonstrated to be scalable to within the scope of real world, [city-wide], problems. This paper compares and contrasts three methods of parallel distribution of the Genetic Algorithm's computational workload: a job farming algorithm and two variations on an `Islands' approach. Results are presented in the paper from both single and mixed mode strategies. The results presented are from a range of previously published academic problem sets. Additionally a real world inspired problem set is evaluated and a visualisation of the optimised output is given.

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References

1984Network Design and Transportation Planning: Models and Algorithms 1986Bus network design 1998Public Transportation Access 1998Urban Bus Transit Route Network Design Using Genetic Algorithm 1980Evaluation and optimization of urban public transportation networks 1995Hybrid route generation heuristic algorithm for the design of transit networks 1967The Design of Routes, Service Frequencies, and Schedules for a Municipal Bus Undertaking: A Case Study 2006Using a Simulated Annealing Algorithm to Solve the Transit Route Network Design Problem

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